Creating species lists
Each country team first will make a list of at least 100 candidate
species for which there is likely some data for indicator 1 and/or 2.‘Likely to have some data’ means that the species are notrecently discovered/ poorly known/ very hard to document population size
(e.g. they are countable by observation, camera trap, etc.), do not have
taxonomic disputes, etc. This is the list of species to try to
collect data for.
Following are two ways to make this list of 100 species, though other
approaches or a blend of approaches is fine.
First, compose a list of species at the country level that a
national biodiversity expert or panel of experts thinks might have
data. Then, ‘cross check’ this list against relevant sources of data
to narrow it down (e.g. removing species for which there are no
published reports, articles, websites, databases, or experts
available). This approach could lead to over-representation of
well-known, flagship, or economically important species.
Choose one or two prominent data sources (e.g. recovery plans or
similar), list all species in that data source, and pick species from
this list in a stratified random fashion to cover taxonomy, habitat,
etc.. For example, this might involve going through recovery plans for
all federally listed Endangered Species, the national Red List, or
other lists of conservation concern (e.g. Annex II, IV and V species
of the EU Habitats Directive- a defined list of policy importance).
This could lead to overrepresentation of species of conservation
concern/ underrepresentation of common or ”least concern” species.
Many countries have Red Lists for various taxonomic groups. These
lists would be one way to select tens to hundreds of species per
country across taxonomic groups and ensure each national RL status is
represented (Endangered, Least Concern, etc). Note: many LC IUCN
species are nevertheless of local or regional conservation concern,
and are declining rapidly, etc. so should not be ignored.
It is vital to document how the list is developed in order to
identify any biases (e.g. mostly common species). In this project, and
in the first use of the indicators by a country for National Reporting,it is acceptable to have some biases , but as data quality and
collection efforts improve, biases should decrease. Surveying multiple
data sources may be needed (for example: scope the Red List to see what
species have data available, then consult with experts on other data
sources).
It is not necessary for all chosen species to have high quality
data across their range. While indicators would be more accurate if all
species have data for all populations, complete population data may only
be available rarely. It is ok if data are available for only one
of the two indicators or for only some populations of a species (as
explained below). Moreover, upon investigation, species initially deemed
likely to have some data, may actually have insufficient data to
calculate either indicator. Species should not be removed from the list
after the initial list is made. We will calculate the indicators with
and without various types and levels of missing data.
There are some species where it will be particularly hard or impossible
to quantify Indicators 1 and 2, and they should be excluded from the
species list. For example, evaluation of the Ne>500
criterion will be hard to implement in species where natural
subpopulations are typically very large and/or hard to measure, such as
microcrustaceans, many insects, some fungi, highly clonal organisms,
some plants with deep soil seed banks (where all ‘individuals’ cannot be
counted). Populations of such species can also grow in a short amount of
time to very large numbers and have large levels of standing genetic
variation (Chaturvedi et al. 2021). We advise not attempting to
include such species in a country’s first evaluation of these indicators
due to difficulty in finding and interpreting data.